Effects of Landscape History and Forest Management on Landslide-Driven Sediment Dynamics in Mountain Drainage Basins of Coastal British Columbia
Bibliographic record
Abstract
Landslide magnitude-frequency (LMF) and yield-area relations are examined for evaluating landslide-driven sediment dynamics in the Tsitika and Eve Rivers (612 km{2}), British Columbia. Research methods couple field work and air photo interpretation (API) in a GIS environment. API covers a seventy year time window. Results show that the long history of glacial erosion and the more recent forest management affect contemporary landslide activity in many respects. First, the nature and distribution of Quaternary-derived surficial deposits confounds primary lithologic effects; therefore, topographies underlain by less resistant geology are not typically associated with higher rates of landsliding. Second, the stratifications of LMF by landslide and terrain attributes have allowed detecting for the first time characteristic landslide length scales dependent on (i) movement style (i.e., slide, avalanche, and flow); (ii) type of material mobilized (i.e., bedrock and debris); and (iii) land use (i.e., forest clearing). As a conclusion, landscape bio-morphometric controls override the theoretical self-organized criticality of LMF relations. Third, slope-area analysis of landslide initiation and deposition zones reveals that bedrock landslides dominate the landscape on mountain summits and ridges; these processes deliver material to colluvial channels, in which debris is temporarily stored until remobilization occurs via full-scale debris flows. In undisturbed forest, during the seventy years examined, colluvial activity across geomorphic process domains (seen as sediment reservoirs) has generated net volume accumulation in unchannelled valleys, sink colluvial, and fluvially-dominated channels; in contrast, planar slopes and gullies have been degrading. Logging operations have accentuated aggradation in gullies and in unchannelled topographies. Finally, the area-based scaling relation of landslide sediment yield appears to match the spatial organization of geomorphic process domains. In this context, the contemporary, specific fluvial sediment yield (suspended) of British Columbia exceeds the specific landslide yield for drainage areas comprised between 5 and 50 km{2}. Cumulative daily yield indicates that colluvial sediment redistribution across landscape scales is limited to relatively small drainage areas; specifically, 90% of the colluvial load is injected within contributing area of about 0.6 km{2}.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".